Cancer is typically diagnosed by analyzing stained samples of tissue from cancer patients and then correlating target patterns in the tissue samples with grading and scoring methods for different kinds of cancers. For example, the Gleason grading system indicates the malignancy of prostate cancer based on the architectural pattern of the glands of a stained prostate tumor. In addition, breast cancer can be diagnosed by grading stained breast tissue using the Allred score, the Elston-Ellis score or the HercepTest™ score. The Allred score indicates the severity of cancer based on the percentage of cells that have been stained to a certain intensity by the estrogen receptor (ER) antibody. The Elston-Ellis score indicates the severity of cancer based on the proportion of tubules in the tissue sample, the similarity of nucleus sizes and the number of dividing cells per high power field of 400× magnification. The HercepTest™ score indicates the severity of cancer based on the level of HER2 protein overexpression as indicated by the degree of membrane staining. The Fuhrman nuclear grading system indicates the severity of renal cell carcinoma (RCC) based on the morphology of the nuclei of kidney cells.
But the various cancer scoring and grading systems can deliver inconsistent results because even an experienced pathologist may misjudge the target patterns and structures in the stained tissue due to fatigue and loss of concentration. Therefore, computer-assisted image analysis systems have been developed to support pathologists in the tedious task of grading and scoring digital images of the stained tissue samples. The digital images are rectangular arrays of pixels. Each pixel is characterized by its position in the array and a plurality of numerical pixel values associated with the pixel. The pixel values represent color or grayscale information for various image layers. For example, grayscale digital images are represented by a single image layer, whereas RGB images are represented by three color image layers. Some existing image analysis systems apply semantic networks to analyze the contents of the digital images. These systems segment, classify and quantify objects present in the images by generating semantic networks that link pixel values to data objects according to class networks. The image analysis systems that apply semantic networks perform object-oriented analysis, as opposed to solely statistical pixel-oriented analysis. Consequently, semantic network systems classify not just pixels, but also the data objects linked to the pixels. The data objects that are linked to the pixels and to one another represent information about the digital images.
Although object-oriented image analysis can provide better results for cancer scoring and grading systems than can pixel-oriented analysis alone, object-oriented analysis is also more computationally involved. Therefore, object-oriented analysis is often slower than statistical pixel-oriented analysis alone. Particularly in digital pathology where each tissue slide can generate gigapixels of data, performing a full-scale object-oriented analysis is too time-consuming. A method is sought that retains the advantages of object-oriented analysis, yet enhances the performance of analysis systems based on computer-implemented semantic networks. Such a method would efficiently manage the computational resources of the object-oriented image analysis systems.